Center for Research Computing, University of Notre Dame
2023-09-05
Building Stuff?
Building Agents based on Large Language Models!
Learn More: Effective testing for machine learning systems
Youtube Discussion: MLOps Chat: How Should We Test ML Models? with Data Scientist Jeremy Jordan
Learn More: Effective testing for machine learning systems
We will focus on Conversational Agents…
Andrej Karpathy, “State of GPT” | BRK216HFS, Microsoft Build, 2023.
Andrej Karpathy, “State of GPT” | BRK216HFS, Microsoft Build, 2023.
Ouyang, Long, Jeff Wu, Xu Jiang, Diogo Almeida, Carroll L. Wainwright, Pamela Mishkin, Chong Zhang, et al.
“Training Language Models to Follow Instructions with Human Feedback.”
arXiv, March 4, 2022. https://doi.org/10.48550/arXiv.2203.02155.
Kojima, Takeshi, Shixiang Shane Gu, Machel Reid, Yutaka Matsuo, and Yusuke Iwasawa.
“Large Language Models Are Zero-Shot Reasoners.” arXiv, January 29, 2023.
Henry Zeng, Lauryn Gayhardt, Jill Grant “What is Azure Machine Learning prompt flow
(preview) - Azure Machine Learning,” Jul. 02, 2023.
http://tiny.cc/kelavz (accessed Sep. 04, 2023).
Henry Zeng, Lauryn Gayhardt, Jill Grant “What is Azure Machine Learning prompt flow
(preview) - Azure Machine Learning,” Jul. 02, 2023.
http://tiny.cc/kelavz (accessed Sep. 04, 2023).
Besta, Maciej, Nils Blach, Ales Kubicek, Robert Gerstenberger, Lukas Gianinazzi, Joanna Gajda, Tomasz Lehmann, et al
“Graph of Thoughts: Solving Elaborate Problems with Large Language Models.” arXiv, August 21, 2023.
“Prompt Engineering Guide – Nextra.”
https://www.promptingguide.ai/ (accessed Sep. 04, 2023).
“Custom Retriever Combining KG Index and VectorStore Index
S. Patil, “Gorilla: Large Language Model Connected with Massive APIs [Project Website].” Sep. 04, 2023.
Accessed: Sep. 04, 2023. [Online]. Available: https://github.com/ShishirPatil/gorilla
S. Patil, “Gorilla: Large Language Model Connected with Massive APIs [Project Website].” Sep. 04, 2023.
Accessed: Sep. 04, 2023. [Online]. Available: https://github.com/ShishirPatil/gorilla
JSON-Grammar
root ::= object
value ::= object | array | string | number | ("true" | "false" | "null") ws
object ::=
"{" ws (
string ":" ws value
("," ws string ":" ws value)*
)? "}" ws
array ::=
"[" ws (
value
("," ws value)*
)? "]" ws
string ::=
"\"" (
[^"\\] |
"\\" (["\\/bfnrt] | "u" [0-9a-fA-F] [0-9a-fA-F] [0-9a-fA-F] [0-9a-fA-F]) # escapes
)* "\"" ws
number ::= ("-"? ([0-9] | [1-9] [0-9]*)) ("." [0-9]+)? ([eE] [-+]? [0-9]+)? ws
# Optional space: by convention, applied in this grammar after literal chars when allowed
ws ::= ([ \t\n] ws)?
“speculative : add grammar support by ggerganov · Pull Request #2991 · ggerganov/llama.cpp,”
GitHub. https://github.com/ggerganov/llama.cpp/pull/2991 (accessed Sep. 04, 2023).
Andrej Karpathy, “State of GPT” | BRK216HFS, Microsoft Build, 2023.
Matt Bronstein and Rajko Radovanovic, “Supporting the Open Source AI Community,”
Andreessen Horowitz, Aug. 30, 2023.
http://tiny.cc/uflavz (accessed Sep. 03, 2023).